HBO

Ashley Wright & Mubeena Wahaj

2022-05-02

Lights, camera, action!

Today, we’re going to take a deep dive into the world of HBO movies and TV shows. From the iconic dramas like The Sopranos and Game of Thrones to the latest releases. HBO has been providing quality content to its viewers for decades, but have you ever wondered how they make decisions about what shows to produce or which movies to acquire? That’s where the fascinating world of HBO data comes into play. By analyzing audience trends, ratings, and viewer demographics, HBO can make informed decisions about what to offer to its loyal fans. So sit back, grab a snack, and get ready to explore the exciting world of HBO data.

Installing packages

#install.packages("ggrepel")
#install.packages("ggiraph") ## to use geom_tooltip
#install.packages("ggiraphExtra")
#load tidyverse to manipulate data
#load ggplot2 for graphing
#load shiny to...
#load dplyer to manipulate data
#load knitr for general-purpose literate programming
#load kableExtra to add features to table

library(ggrepel) ## For using tooltip
library(ggiraph)  ## For using geom_tooltip
library(ggiraphExtra)
library(tidyverse)
library(ggplot2)
library(shiny)
library(dplyr)
library(countrycode)
library(knitr)
library(kableExtra)
library(maps)

About Our Data

The data we’ve decided to work on is from kaggle and is owned by Diego Enrique and here’s the link: https://www.kaggle.com/datasets/dgoenrique/hbo-max-movies-and-tv-shows

Titles data:

15 variables, 3030 observations

id: The title ID

title: The name of the title

show_type: Tv show or Movie

description: A description of movie or tv show

release_year: Year show/movie was released

age_certification: The age rating of movie or show

runtime: The length of the episode of show or movie in minutes

genres: A list of genres

production_countries: Countries that produced the show/movie

seasons: Number of seasons IF it is a show

imdb_id: The title ID on IMDB

imdb_score: Score on IMDB

imdb_votes: Votes on IMDB

tmdb_popularity: Popularity on TMDB

tmdb_score: Score on TMDB

Credits data:

5 variables, 64879 observations

person_ID: The person ID on JustWatch

id: The title ID on JustWatch

name: The name of actor or director

character_name: The name of character played in movie/show

role: ACTOR or DIRECTOR

Let us read our datas, shall we?

We’re using the kable and head function to show a part of the data sets we’re working on but in an organized manner

Here’s our credits.csv

Sample table of credits data
person_id id name character role
14701 tm77588 Humphrey Bogart Rick Blaine ACTOR
14702 tm77588 Ingrid Bergman Ilsa Lund ACTOR
14703 tm77588 Paul Henreid Victor Laszlo ACTOR
14704 tm77588 Claude Rains Captain Louis Renault ACTOR
14705 tm77588 Conrad Veidt Major Heinrich Strasser ACTOR
14706 tm77588 Sydney Greenstreet Signor Ferrari ACTOR

And here’s our titles.csv

Sample table of titles data
id title type release_year age_certification runtime genres production_countries seasons imdb_id imdb_score imdb_votes tmdb_popularity tmdb_score
tm77588 Casablanca MOVIE 1943 PG 102 [‘drama’, ‘romance’, ‘war’] [‘US’] NA tt0034583 8.5 577842 22.005 8.167
tm155702 The Wizard of Oz MOVIE 1939 G 102 [‘fantasy’, ‘family’] [‘US’] NA tt0032138 8.1 406105 56.631 7.583
tm83648 Citizen Kane MOVIE 1941 PG 119 [‘drama’] [‘US’] NA tt0033467 8.3 446627 19.900 8.022
tm3175 Meet Me in St. Louis MOVIE 1945 113 [‘drama’, ‘family’, ‘romance’, ‘music’, ‘comedy’] [‘US’] NA tt0037059 7.5 25589 8.311 7.000
ts225761 Tom and Jerry SHOW 1940 8 [‘animation’, ‘comedy’, ‘family’, ‘action’] [‘US’] 16 tt6422744 7.7 859 1.400 10.000
tm156463 Gone with the Wind MOVIE 1940 G 238 [‘drama’, ‘romance’, ‘war’, ‘history’] [‘US’] NA tt0031381 8.2 319463 27.535 8.000

What if we try to combine these data sets?

both_data <- inner_join(titles, credits, by = "id")

kable(head(both_data),
      align = "c",
      caption = "Sample table of both data",
      format = "html")
Sample table of both data
id title type release_year age_certification runtime genres production_countries seasons imdb_id imdb_score imdb_votes tmdb_popularity tmdb_score person_id name character role
tm77588 Casablanca MOVIE 1943 PG 102 [‘drama’, ‘romance’, ‘war’] [‘US’] NA tt0034583 8.5 577842 22.005 8.167 14701 Humphrey Bogart Rick Blaine ACTOR
tm77588 Casablanca MOVIE 1943 PG 102 [‘drama’, ‘romance’, ‘war’] [‘US’] NA tt0034583 8.5 577842 22.005 8.167 14702 Ingrid Bergman Ilsa Lund ACTOR
tm77588 Casablanca MOVIE 1943 PG 102 [‘drama’, ‘romance’, ‘war’] [‘US’] NA tt0034583 8.5 577842 22.005 8.167 14703 Paul Henreid Victor Laszlo ACTOR
tm77588 Casablanca MOVIE 1943 PG 102 [‘drama’, ‘romance’, ‘war’] [‘US’] NA tt0034583 8.5 577842 22.005 8.167 14704 Claude Rains Captain Louis Renault ACTOR
tm77588 Casablanca MOVIE 1943 PG 102 [‘drama’, ‘romance’, ‘war’] [‘US’] NA tt0034583 8.5 577842 22.005 8.167 14705 Conrad Veidt Major Heinrich Strasser ACTOR
tm77588 Casablanca MOVIE 1943 PG 102 [‘drama’, ‘romance’, ‘war’] [‘US’] NA tt0034583 8.5 577842 22.005 8.167 14706 Sydney Greenstreet Signor Ferrari ACTOR

Let’s begin by determining the number or movies and TV shows we are working with

Type Count
MOVIE 2408
SHOW 622

Wow! that’s a lot more movies than shows! But let’s see it visually

What’s the distribution of genres for both Shows and Movies in our dataset?

Here’s the table of number of genres in descending order

Number of movies and TV shows by genre
genres type Count
documentation MOVIE 427
drama MOVIE 406
drama MOVIE 379
comedy MOVIE 271
thriller MOVIE 260
comedy MOVIE 193
crime MOVIE 186
drama MOVIE 175
action MOVIE 167
drama SHOW 153
action MOVIE 146
comedy SHOW 135
european MOVIE 134
comedy MOVIE 129
romance MOVIE 126
comedy MOVIE 121
drama MOVIE 108
thriller MOVIE 103
horror MOVIE 98
romance MOVIE 95
crime MOVIE 90
european MOVIE 89
history MOVIE 89
romance MOVIE 87
family MOVIE 85
crime SHOW 76
scifi MOVIE 75
thriller MOVIE 71
fantasy MOVIE 69
action MOVIE 67
crime MOVIE 62
animation MOVIE 60
fantasy MOVIE 60
music MOVIE 59
action MOVIE 58
documentation SHOW 58
scifi MOVIE 58
scifi MOVIE 54
documentation MOVIE 53
romance MOVIE 53
thriller SHOW 53
comedy SHOW 51
documentation MOVIE 51
war MOVIE 50
drama SHOW 49
animation MOVIE 48
family MOVIE 48
fantasy MOVIE 47
sport MOVIE 47
fantasy MOVIE 44
action SHOW 42
documentation MOVIE 40
scifi SHOW 40
european MOVIE 38
animation MOVIE 37
animation SHOW 35
thriller MOVIE 35
animation SHOW 34
comedy MOVIE 33
history MOVIE 33
family MOVIE 32
family SHOW 32
horror MOVIE 32
music MOVIE 32
european MOVIE 31
comedy SHOW 30
drama SHOW 29
fantasy SHOW 29
history MOVIE 28
war MOVIE 28
comedy SHOW 25
scifi MOVIE 25
drama MOVIE 24
animation MOVIE 23
crime SHOW 23
documentation SHOW 23
family SHOW 23
romance SHOW 23
action SHOW 22
action SHOW 22
animation SHOW 22
history SHOW 22
reality SHOW 22
scifi SHOW 22
crime MOVIE 20
horror SHOW 20
music MOVIE 20
scifi SHOW 20
scifi SHOW 20
sport MOVIE 20
comedy SHOW 19
documentation SHOW 19
music MOVIE 19
animation SHOW 18
action SHOW 17
animation MOVIE 17
documentation MOVIE 17
drama SHOW 17
fantasy MOVIE 17
fantasy SHOW 17
horror MOVIE 17
romance SHOW 17
reality SHOW 16
romance MOVIE 16
sport MOVIE 16
war MOVIE 16
fantasy SHOW 15
thriller SHOW 15
western MOVIE 15
family MOVIE 14
fantasy SHOW 14
reality SHOW 13
sport MOVIE 13
western MOVIE 13
action MOVIE 12
war MOVIE 12
history MOVIE 11
music MOVIE 11
scifi MOVIE 11
comedy SHOW 10
documentation SHOW 10
european SHOW 10
family SHOW 10
horror MOVIE 10
reality SHOW 10
sport SHOW 10
war SHOW 10
western MOVIE 10
thriller SHOW 9
thriller SHOW 9
animation SHOW 8
european MOVIE 8
music SHOW 8
sport SHOW 8
animation SHOW 7
action SHOW 6
animation SHOW 6
crime SHOW 6
family SHOW 6
romance SHOW 6
thriller SHOW 6
crime SHOW 5
crime SHOW 5
drama MOVIE 5
family SHOW 5
family SHOW 5
horror SHOW 5
western MOVIE 5
comedy SHOW 4
drama SHOW 4
drama SHOW 4
fantasy SHOW 4
history MOVIE 4
reality SHOW 4
romance SHOW 4
comedy MOVIE 3
documentation SHOW 3
european SHOW 3
family MOVIE 3
family SHOW 3
history SHOW 3
horror MOVIE 3
horror SHOW 3
horror SHOW 3
romance MOVIE 3
scifi SHOW 3
sport SHOW 3
thriller MOVIE 3
war SHOW 3
crime SHOW 2
drama SHOW 2
european MOVIE 2
european SHOW 2
family SHOW 2
fantasy SHOW 2
history SHOW 2
horror SHOW 2
music SHOW 2
music SHOW 2
scifi SHOW 2
war MOVIE 2
war SHOW 2
western MOVIE 2
action SHOW 1
action SHOW 1
animation SHOW 1
crime MOVIE 1
crime MOVIE 1
crime SHOW 1
documentation MOVIE 1
documentation SHOW 1
drama SHOW 1
european SHOW 1
fantasy SHOW 1
history MOVIE 1
history SHOW 1
horror MOVIE 1
horror SHOW 1
horror SHOW 1
music SHOW 1
music SHOW 1
music SHOW 1
reality MOVIE 1
reality MOVIE 1
reality MOVIE 1
reality SHOW 1
romance SHOW 1
romance SHOW 1
scifi SHOW 1
sport MOVIE 1
sport SHOW 1
sport SHOW 1
thriller MOVIE 1
thriller SHOW 1
thriller SHOW 1
western SHOW 1
western SHOW 1

Since we just finished oberserving the number of genres in our dataset

Let us see if there’s a correlation between age_restriction and genres

## Unique age certifications:  PG, G, PG-13, R, TV-G, TV-Y, TV-Y7, TV-PG, NC-17, TV-14, TV-MA, TV-Y7-FV

Well that did not work as expected. Let’s see if a geom_tile graph does the job:

Here are the number of shows available in HBO by release year

Here’s the summary table of what the graph shows

decade type count
1900s MOVIE 8
1910s MOVIE 12
1920s MOVIE 35
1920s SHOW 1
1930s MOVIE 44
1940s MOVIE 57
1940s SHOW 1
1950s MOVIE 91
1960s MOVIE 130
1960s SHOW 6
1970s MOVIE 109
1970s SHOW 3
1980s MOVIE 170
1980s SHOW 6
1990s MOVIE 265
1990s SHOW 43
2000s MOVIE 395
2000s SHOW 94
2010s MOVIE 706
2010s SHOW 230
2020s MOVIE 274
2020s SHOW 148
NA MOVIE 112
NA SHOW 90

This indicates us that HBO primarily features Movies and Shows from the decade of 2010s

You can see there is a wide range of movies and tv shows, especially what year they were released. I wonder what the oldest movies and shows are?

Oldest Movie on HBO
title release_year genres
The Prince of Magicians 1901 [‘comedy’]
Oldest Show on HBO
title release_year genres
Looney Tunes 1929 [‘comedy’, ‘family’, ‘thriller’, ‘animation’]

Now explore if there’s a relationship between longest movie and its popularity?

movies_runtime = titles %>% 
  filter(type == "MOVIE") 

ggplot(movies_runtime, aes(x=runtime , y= average_score , tooltip = title)) + 
  geom_point(color= "grey") + 
  xlab("Duration in minutes") +
  ylab("Popularity based on averages IMDB and TMDB scores)")+
  ggtitle("Relationship between longest movie and its popularity")+
  theme(plot.title = element_text(hjust = 0.5))+
  theme_bw()

  ##geom_tooltip()
shortest_movie <- titles %>%
  filter(type == "MOVIE") %>%
  arrange(runtime) %>%
  select(title, type,runtime, release_year, genres) %>%
  head(1)

shortest_movie
##                          title  type runtime release_year genres
## 1 An Impossible Balancing Feat MOVIE       1         1902     []
longest_movie <- titles %>%
  filter(type == "MOVIE") %>%
  arrange(desc(runtime)) %>%
  select(title, type, runtime, release_year, genres) %>%
  head(1)

longest_movie
##                    title  type runtime release_year                genres
## 1 Scenes from a Marriage MOVIE     299         1974 ['drama', 'european']
shortest_show <- titles %>%
  filter(type == "SHOW") %>%
  arrange(runtime, seasons) %>%
  select(title, type, runtime, seasons, release_year, genres) %>%
  head(1)

shortest_show
##                title type runtime seasons release_year                  genres
## 1 Meet the Batwheels SHOW       2       1         2022 ['animation', 'action']
longest_show <- titles %>%
  filter(type == "SHOW") %>%
  arrange(desc(runtime)) %>%
  arrange(desc(seasons)) %>%
  select(title, type, runtime, seasons, release_year, genres) %>%
  head(1)

longest_show
##           title type runtime seasons release_year
## 1 Sesame Street SHOW      51      53         1969
##                                                  genres
## 1 ['comedy', 'animation', 'family', 'fantasy', 'music']

Now lets look at the credits data.

credits %>%
  count(role)
##       role     n
## 1    ACTOR 62158
## 2 DIRECTOR  2721

Are any of these actors/directors in multiple projects? If so, who was in the most projects?

project_count <- credits %>%
  count(name)

glimpse(project_count)
## Rows: 45,276
## Columns: 2
## $ name <chr> "'Auntie' Mackay", "'Little Man' Machan", "'Weird Al' Yankovic", …
## $ n    <int> 1, 3, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1,…
most_projects <- credits %>% 
  count(name) %>% 
  slice_max(n)

most_projects
##           name  n
## 1 Grey DeLisle 60
##Who is this person?

credits %>% 
  filter(name == "Grey DeLisle")
##    person_id        id         name
## 1      14142   ts21507 Grey DeLisle
## 2      14142   ts21601 Grey DeLisle
## 3      14142   ts20381 Grey DeLisle
## 4      14142   ts22480 Grey DeLisle
## 5      14142    ts5042 Grey DeLisle
## 6      14142   tm94209 Grey DeLisle
## 7      14142   tm93784 Grey DeLisle
## 8      14142   tm58727 Grey DeLisle
## 9      14142  tm656353 Grey DeLisle
## 10     14142   tm43061 Grey DeLisle
## 11     14142   tm23583 Grey DeLisle
## 12     14142  tm140798 Grey DeLisle
## 13     14142   ts20574 Grey DeLisle
## 14     14142  tm167763 Grey DeLisle
## 15     14142   tm62126 Grey DeLisle
## 16     14142   tm30342 Grey DeLisle
## 17     14142  tm167388 Grey DeLisle
## 18     14142   tm65596 Grey DeLisle
## 19     14142  tm160240 Grey DeLisle
## 20     14142  tm160231 Grey DeLisle
## 21     14142  tm151689 Grey DeLisle
## 22     14142  tm179476 Grey DeLisle
## 23     14142  tm177932 Grey DeLisle
## 24     14142    ts3139 Grey DeLisle
## 25     14142  tm159051 Grey DeLisle
## 26     14142  tm171655 Grey DeLisle
## 27     14142   tm63619 Grey DeLisle
## 28     14142  tm152510 Grey DeLisle
## 29     14142  tm152501 Grey DeLisle
## 30     14142   ts37956 Grey DeLisle
## 31     14142  tm195247 Grey DeLisle
## 32     14142  tm244555 Grey DeLisle
## 33     14142  tm238389 Grey DeLisle
## 34     14142  tm193859 Grey DeLisle
## 35     14142  tm244564 Grey DeLisle
## 36     14142  tm219341 Grey DeLisle
## 37     14142  tm214711 Grey DeLisle
## 38     14142  tm244119 Grey DeLisle
## 39     14142  tm244479 Grey DeLisle
## 40     14142  tm138025 Grey DeLisle
## 41     14142  tm365731 Grey DeLisle
## 42     14142  tm422417 Grey DeLisle
## 43     14142  tm301058 Grey DeLisle
## 44     14142  tm372838 Grey DeLisle
## 45     14142  tm361837 Grey DeLisle
## 46     14142  tm326858 Grey DeLisle
## 47     14142  tm414009 Grey DeLisle
## 48     14142  tm405461 Grey DeLisle
## 49     14142  tm317933 Grey DeLisle
## 50     14142  tm423754 Grey DeLisle
## 51     14142  tm820756 Grey DeLisle
## 52     14142   ts89867 Grey DeLisle
## 53     14142  tm894108 Grey DeLisle
## 54     14142  tm883958 Grey DeLisle
## 55     14142 tm1248448 Grey DeLisle
## 56     14142 tm1028015 Grey DeLisle
## 57     14142 tm1065433 Grey DeLisle
## 58     14142  tm930306 Grey DeLisle
## 59     14142 tm1171238 Grey DeLisle
## 60     14142  tm987899 Grey DeLisle
##                                                                           character
## 1                                                              Daphne Blake (voice)
## 2                                                        The High Priestess (voice)
## 3                                                              Daphne Blake (voice)
## 4                                                                     Mandy (voice)
## 5                                                  Frances 'Frankie' Foster (voice)
## 6                                                                    Daphne (voice)
## 7                                                              Daphne Blake (voice)
## 8                                             Daphne / Cat Witch / Honeybee (voice)
## 9  Frankie Foster / Tiny Friend / Little Boy Voice / Lady (voice) (as Grey DeLisle)
## 10                                        Crazy Old Cat Lady/Gramma Stuffum (voice)
## 11                                                                   Daphne (voice)
## 12                                                                   Daphne (voice)
## 13                                                                                 
## 14                                                           Barbara Gordon (voice)
## 15                                             Anchor Carla / Female Mutant (voice)
## 16                                                        Lois Lane / Queen (voice)
## 17                                       Ree'Yu / Ardakian Trawl / Boodikka (voice)
## 18                                                         Young Manchester (voice)
## 19                                                             Daphne Blake (voice)
## 20                                                             Daphne Blake (voice)
## 21                                            Grandmother (voice) (as Grey Griffin)
## 22                    Nora Allen / Young Barry Allen / Martha Wayne / Joker (voice)
## 23                                                             Anchor Carla (voice)
## 24                                                Margaret Sorrow / Magpie  (voice)
## 25                       Wonder Woman / Superbaby (voice) (as Grey DeLisle Griffin)
## 26                                                             Daphne Blake (voice)
## 27                                                             Daphne Blake (voice)
## 28                                                             Daphne Blake (voice)
## 29                                                             Daphne Blake (voice)
## 30                                                             Daphne Blake (voice)
## 31                                                          Tina / Platinum (voice)
## 32                                                             Daphne Blake (voice)
## 33                                                             Wonder Woman (voice)
## 34                                                                 Samantha (voice)
## 35                                                             Wonder Woman (voice)
## 36                                                 Wonder Woman / Lois Lane (voice)
## 37                                                             Daphne Blake (voice)
## 38                                                             Daphne Blake (voice)
## 39                                                             Wonder Woman (voice)
## 40                                                             Daphne Blake (voice)
## 41                                Sister Leslie / Jason / Additional Voices (voice)
## 42                                                             Daphne Blake (voice)
## 43                                                             Daphne Blake (voice)
## 44                        Wonder Woman / Diana Prince (voice) and Lois Lane (voice)
## 45                                              Daphne Blake / Black Canary (voice)
## 46                                                             Daphne Blake (voice)
## 47           Diana Prince / Wonder Woman (voice) / Lois Lane (voice) / Ring (voice)
## 48                                                         Wonder Woman / Lois Lane
## 49                                                  Wonder Woman / Platinum (voice)
## 50                                                             Wonder Woman (voice)
## 51                                                               Mrs. Claus (Voice)
## 52                                                             Daphne Blake (voice)
## 53                                                        Additional Voices (voice)
## 54                                         Wonder Woman (voice) / Lois Lane (voice)
## 55                                     Daphne / Daisy / Musketeer 1 / Olive (voice)
## 56                                   Beelzebub / Little Della / Little Jack (voice)
## 57                                         Daphne Blake / Frau Glockenspiel (voice)
## 58                                                                 Lady Eve (voice)
## 59                                              Diana Prince / Wonder Woman (voice)
## 60                                                             Daphne Blake (voice)
##     role
## 1  ACTOR
## 2  ACTOR
## 3  ACTOR
## 4  ACTOR
## 5  ACTOR
## 6  ACTOR
## 7  ACTOR
## 8  ACTOR
## 9  ACTOR
## 10 ACTOR
## 11 ACTOR
## 12 ACTOR
## 13 ACTOR
## 14 ACTOR
## 15 ACTOR
## 16 ACTOR
## 17 ACTOR
## 18 ACTOR
## 19 ACTOR
## 20 ACTOR
## 21 ACTOR
## 22 ACTOR
## 23 ACTOR
## 24 ACTOR
## 25 ACTOR
## 26 ACTOR
## 27 ACTOR
## 28 ACTOR
## 29 ACTOR
## 30 ACTOR
## 31 ACTOR
## 32 ACTOR
## 33 ACTOR
## 34 ACTOR
## 35 ACTOR
## 36 ACTOR
## 37 ACTOR
## 38 ACTOR
## 39 ACTOR
## 40 ACTOR
## 41 ACTOR
## 42 ACTOR
## 43 ACTOR
## 44 ACTOR
## 45 ACTOR
## 46 ACTOR
## 47 ACTOR
## 48 ACTOR
## 49 ACTOR
## 50 ACTOR
## 51 ACTOR
## 52 ACTOR
## 53 ACTOR
## 54 ACTOR
## 55 ACTOR
## 56 ACTOR
## 57 ACTOR
## 58 ACTOR
## 59 ACTOR
## 60 ACTOR

Here’s genre vs popularity graph based on movies and shows

genre_popularity <- titles %>%
  mutate(genres = str_remove_all(genres, "'")) %>% 
  mutate(genres = gsub("\\[", "", genres)) %>% 
  mutate(genres = gsub("\\]", "", genres)) %>% 
  separate_rows(genres, sep = ", ") %>%
  group_by(genres, type,tmdb_popularity,tmdb_score, imdb_score ) %>%
  summarize(Count = n()) %>%
  ungroup() %>%
  arrange(desc(tmdb_popularity))

genre_popularity
## # A tibble: 7,598 × 6
##    genres   type  tmdb_popularity tmdb_score imdb_score Count
##    <chr>    <chr>           <dbl>      <dbl>      <dbl> <int>
##  1 action   SHOW            3481.       8.80        9.1     1
##  2 drama    SHOW            3481.       8.80        9.1     1
##  3 horror   SHOW            3481.       8.80        9.1     1
##  4 scifi    SHOW            3481.       8.80        9.1     1
##  5 thriller SHOW            3481.       8.80        9.1     1
##  6 action   MOVIE            696.       7.13        6.3     1
##  7 fantasy  MOVIE            696.       7.13        6.3     1
##  8 scifi    MOVIE            696.       7.13        6.3     1
##  9 action   SHOW             559.       8.4         9.2     1
## 10 drama    SHOW             559.       8.4         9.2     1
## # … with 7,588 more rows
# Create the bar chart
ggplot(genre_popularity, aes(x = reorder(genres, Count), y = tmdb_popularity, fill = type)) +
  geom_bar(stat = "identity")  +
  labs(x = "Genre", y = "tmdb_popularity", title = "Genres and its popularity") +
  theme_light()+coord_flip()

Who would’ve know?!

Let us look at the Number of movies and TV shows by country

Unfortunately, because HBO only got their movies and shows from 99 countries, there are some uncolored countries

Number of movies and TV shows by country
production_countries type total full_country_name
US MOVIE 1824 United States
US SHOW 491 United States
GB MOVIE 270 United Kingdom
FR MOVIE 178 France
JP MOVIE 112 Japan
DE MOVIE 87 Germany
CA MOVIE 75 Canada
IT MOVIE 54 Italy
GB SHOW 38 United Kingdom
ES MOVIE 28 Spain
MX MOVIE 26 Mexico
AU MOVIE 24 Australia
SE MOVIE 22 Sweden
IN MOVIE 20 India
ES SHOW 19 Spain
CH MOVIE 16 Switzerland
HK MOVIE 14 Hong Kong SAR China
CN MOVIE 13 China
DK MOVIE 13 Denmark
BE MOVIE 12 Belgium
BR SHOW 12 Brazil
NZ MOVIE 12 New Zealand
PL MOVIE 11 Poland
SU MOVIE 11 NA
ZA MOVIE 10 South Africa
AR MOVIE 8 Argentina
AT MOVIE 8 Austria
IE MOVIE 8 Ireland
CA SHOW 7 Canada
JP SHOW 7 Japan
NL MOVIE 7 Netherlands
AE MOVIE 6 United Arab Emirates
AR SHOW 6 Argentina
MX SHOW 6 Mexico
PR MOVIE 6 Puerto Rico
SG SHOW 6 Singapore
TW SHOW 6 Taiwan
BG MOVIE 5 Bulgaria
CO MOVIE 5 Colombia
DE SHOW 5 Germany
FR SHOW 5 France
IL MOVIE 5 Israel
IL SHOW 5 Israel
SN MOVIE 5 Senegal
BR MOVIE 4 Brazil
CZ MOVIE 4 Czechia
DO MOVIE 4 Dominican Republic
GR MOVIE 4 Greece
IT SHOW 4 Italy
KR MOVIE 4 South Korea
XC MOVIE 4 NA
BO MOVIE 3 Bolivia
CL MOVIE 3 Chile
CL SHOW 3 Chile
CU MOVIE 3 Cuba
EC MOVIE 3 Ecuador
HU MOVIE 3 Hungary
IS MOVIE 3 Iceland
NO MOVIE 3 Norway
PT MOVIE 3 Portugal
UY MOVIE 3 Uruguay
CZ SHOW 2 Czechia
DZ MOVIE 2 Algeria
FI MOVIE 2 Finland
ID SHOW 2 Indonesia
IR MOVIE 2 Iran
LU MOVIE 2 Luxembourg
NG MOVIE 2 Nigeria
PE MOVIE 2 Peru
PK MOVIE 2 Pakistan
PL SHOW 2 Poland
RO MOVIE 2 Romania
RO SHOW 2 Romania
RU MOVIE 2 Russia
TH MOVIE 2 Thailand
TR MOVIE 2 Turkey
AF MOVIE 1 Afghanistan
AU SHOW 1 Australia
BS MOVIE 1 Bahamas
CN SHOW 1 China
DK SHOW 1 Denmark
EG MOVIE 1 Egypt
GT MOVIE 1 Guatemala
HU SHOW 1 Hungary
IN SHOW 1 India
KH MOVIE 1 Cambodia
MA MOVIE 1 Morocco
MC MOVIE 1 Monaco
MK MOVIE 1 North Macedonia
NZ SHOW 1 New Zealand
PA MOVIE 1 Panama
PH MOVIE 1 Philippines
PH SHOW 1 Philippines
PY MOVIE 1 Paraguay
RU SHOW 1 Russia
RW MOVIE 1 Rwanda
SG MOVIE 1 Singapore
UA MOVIE 1 Ukraine
UY SHOW 1 Uruguay